{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas Inflow Performance Analysis\n",
    "\n",
    "> Divyanshu Vyas | Oil and Gas Data Science (Machine Learning). \n",
    "\n",
    "This code is useful to quickly find out the parameters and model an IPR for a gas reservoir, with just a few test data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement: \n",
    "A gas well was tested using a three-point conventional deliverability\n",
    "test. Data recorded during the test are given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Pwf' : [1952,1700,1500,1300] , \n",
    "       'm(P)' : [316*10**6 , 245*10**6, 191*10**6, 141*10**6],\n",
    "       'Qg(Mscf/d)' : [0, 2624.6, 4154.7 , 5425.1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pwf</th>\n",
       "      <th>m(P)</th>\n",
       "      <th>Qg(Mscf/d)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>316000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1700</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2624.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500</td>\n",
       "      <td>191000000</td>\n",
       "      <td>4154.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1300</td>\n",
       "      <td>141000000</td>\n",
       "      <td>5425.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pwf       m(P)  Qg(Mscf/d)\n",
       "0  1952  316000000         0.0\n",
       "1  1700  245000000      2624.6\n",
       "2  1500  191000000      4154.7\n",
       "3  1300  141000000      5425.1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.regplot(x='Pwf' , y='m(P)' , data=df, color='r', marker='X', ci=95)\n",
    "\n",
    "plt.title('Real gas Potential v/s Pressure(Available Test Data)')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Back-Pressure Equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pwf2'] = df['Pwf']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = 1952\n",
    "\n",
    "df['Pr2 - Pwf2'] = pr**2 - df['Pwf']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pwf</th>\n",
       "      <th>m(P)</th>\n",
       "      <th>Qg(Mscf/d)</th>\n",
       "      <th>Pwf2</th>\n",
       "      <th>Pr2 - Pwf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>316000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3810304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1700</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2624.6</td>\n",
       "      <td>2890000</td>\n",
       "      <td>920304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500</td>\n",
       "      <td>191000000</td>\n",
       "      <td>4154.7</td>\n",
       "      <td>2250000</td>\n",
       "      <td>1560304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1300</td>\n",
       "      <td>141000000</td>\n",
       "      <td>5425.1</td>\n",
       "      <td>1690000</td>\n",
       "      <td>2120304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pwf       m(P)  Qg(Mscf/d)     Pwf2  Pr2 - Pwf2\n",
       "0  1952  316000000         0.0  3810304           0\n",
       "1  1700  245000000      2624.6  2890000      920304\n",
       "2  1500  191000000      4154.7  2250000     1560304\n",
       "3  1300  141000000      5425.1  1690000     2120304"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now plot (Pr2 - Pwf2) vs Qg on a Log Log scale\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.loglog(df['Pr2 - Pwf2'] , df['Qg(Mscf/d)'])\n",
    "\n",
    "plt.scatter(df['Pr2 - Pwf2'] , df['Qg(Mscf/d)'] , color='r')\n",
    "\n",
    "plt.xlabel('Qg(Mscf/d)')\n",
    "plt.ylabel('Pr2 - Pwf2')\n",
    "\n",
    "plt.title('Log Log del P^2 vs Qg')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Pause and have a look at the equations :-\n",
    "\n",
    ">### $ Q_g = C(P_r^{2} - P_{wf}^{2})^{n}$\n",
    "\n",
    "> ### $ log(Qg) = log(C) + n*log(P_r^{2} - P_{wf}^{2}) $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.log(df['Pr2 - Pwf2'][1:])\n",
    "\n",
    "y = np.log(df['Qg(Mscf/d)'][1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "eqn = np.polyfit(x,y, deg=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.86999855, -4.07453279])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eqn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing the Results - \n",
    "\n",
    "n = eqn[0]\n",
    "\n",
    "C = np.exp(eqn[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017000155494749536"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So we have successfully calculated the C & n.\n",
    "\n",
    "### $ Q_g = 0.017*(P_r^{2} - P_{wf}^{2})^{0.87} $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's find out the AOF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwf = np.linspace(0,pr,25)\n",
    "\n",
    "qg = C*((pr**2 - pwf**2)**n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.plot(qg,pwf, color='green',lw=3, label='Model Predictions')\n",
    "\n",
    "plt.scatter(df['Qg(Mscf/d)'], df['Pwf'], color='r', marker='X' , s=90, label='Given Test data')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Qg(Mscf/day)')\n",
    "plt.ylabel('Pwf(Psi)')\n",
    "\n",
    "plt.title('Gas-IPR by the C&n model')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
